What a Principal } should know

Defines the analytics platform performance optimization strategy. Architects approaches for processing petabyte datasets: incremental models, approximate algorithms (HyperLogLog for distinct count), materialized views for heavy aggregations.

Defines the analytics platform technical quality strategy. Establishes reliability metrics: data freshness SLA, test coverage for critical models, automated data quality checks. Plans migrations between dbt versions and warehouses.

Architects the platform data storage architecture: columnar formats for OLAP, semi-structured JSON for event data, graph structures for data lineage. Defines the serialization strategy for interoperability between Snowflake, BigQuery, and Spark.

Architects real-time analytics: streaming ingestion into the data warehouse, Kafka-based change data capture, event-driven update of materialized views. Defines governance for event data and their transformations.

Architects a data discovery platform based on Elasticsearch: semantic search over metadata, related model recommendations, full-text search across SQL definitions and documentation. Integrates with dbt docs and BI catalogs.

Architects the analytics platform API layer: metrics store API, semantic layer endpoints, data marketplace. Defines the API strategy for business user self-service data access through query engines.

Architects the analytics caching infrastructure: multi-tier caching (warehouse result cache, application cache, CDN for embedded dashboards). Defines cache consistency strategy for incremental data updates.

Architects real-time analytics on ClickHouse: cluster configuration, replication, distributed tables for scaling. Defines when to use ClickHouse vs warehouse (Snowflake/BigQuery) for different analytical tasks.

Architects the enterprise data modeling strategy: unified semantic layer via dbt metrics/MetricFlow, data vault for historical storage, domain-driven models. Defines the balance between normalization and denormalization for different consumers.

Architects the platform-level data access optimization strategy: automatic clustering, search optimization in Snowflake, BI Engine in BigQuery. Defines cost-based optimization approaches for analytical queries.

Defines the analytical model evolution strategy: schema change versioning through dbt, backward-compatible migrations for BI dashboards. Architects zero-downtime migration processes between warehouse platforms (Redshift→Snowflake, BigQuery).

Architects PostgreSQL's role in the analytics platform: as an operational datastore, as a metadata backend for tools (Airflow, dbt). Defines the data migration strategy from PostgreSQL to the analytical warehouse.

Architects the analytics warehouse cost optimization strategy: multi-cluster configurations, auto-suspend policies, query routing. Defines architectural decisions balancing cost and analytics performance.

Architects the unified data documentation platform: integrating dbt docs, API catalog, and BI glossary into a single data portal. Defines the data literacy and self-service documentation access strategy for business users.

Architects the external data integration strategy for the analytics platform: managed vs self-hosted connectors, API gateway for rate management, schema evolution for third-party APIs. Defines governance for external data sources.

Architects a multi-cloud analytics platform: AWS data lake + Snowflake/BigQuery for compute, cross-cloud data sharing. Defines vendor lock-in mitigation strategy and analytical model portability between clouds.

Architects the analytics platform container strategy: Kubernetes orchestration for dbt jobs and data pipelines, auto-scaling for batch processing, resource management for different workload types (ELT, BI, ML).

Architects the continuous delivery platform for analytics: multi-project CI with shared dbt packages, cross-team dependency management, automated rollback on data quality failures. Defines deployment strategy for critical business models.

Architects the end-to-end data testing platform: automated data observability, anomaly detection at each layer, reconciliation with source of truth. Defines SLA for detecting and resolving data quality issues.

Architects the analytics platform quality assurance strategy: multi-layer testing (unit → integration → acceptance), automated KPI reconciliation with source systems, chaos testing for data pipelines.

Architects enterprise analytics platform orchestration: Airflow/Dagster for multi-project dbt, event-driven triggers, cross-team dependency management. Defines migration strategy to managed orchestration (dbt Cloud, Dagster Cloud).

Architects enterprise BI: multi-tool strategy for different audiences, embedded analytics for products, real-time dashboards. Defines the roadmap from traditional BI to self-service analytics and metrics layer.

Architects next-gen orchestration: Dagster's asset-based approach for a unified analytics platform, integration with dbt mesh, declarative scheduling. Defines migration strategy from Airflow to asset-centric orchestration.

Architects a unified enterprise data discovery platform: automatic lineage from sources to BI, semantic search by business terms, data marketplace for self-service. Defines governance processes for cataloging and data stewardship.

Architects the enterprise data contracts system: contract format standards, automated enforcement through CI/CD, integration with data mesh. Defines governance for contract evolution and breaking changes in analytical models.

Architects the enterprise lakehouse: Delta Lake/Iceberg as open table format, integration with dbt for transformations, unified governance. Defines the strategy for combining data lake and warehouse for different analytical workloads.

Architects enterprise data lineage: automatic tracking from source systems through ETL/ELT to BI dashboards, cross-tool lineage (Airflow → dbt → Tableau). Defines lineage strategy for compliance (GDPR, SOX) and data governance.

Architects the enterprise data quality strategy: unified quality framework for all sources and transformations, ML-driven anomaly detection, automated root cause analysis. Defines a quality-as-code approach with version-controlled rules.

Architects the enterprise data warehouse strategy: multi-warehouse for different workloads (analytics, ML, reporting), cost governance through resource monitors. Defines data sharing architecture between business units and external partners.

Architects the enterprise dbt platform evolution strategy: multi-project mono-repo vs multi-repo, dbt mesh for cross-team dependencies, migration to dbt Cloud. Defines the roadmap for MetricFlow/Semantic Layer adoption to unify business metrics.

Architects the transformation tool selection strategy: dbt (SQL) as primary, Python models for ML feature engineering and complex logic. Defines the integration architecture for pandas/polars/PySpark with dbt for hybrid pipelines.

Architects the enterprise transformation layer strategy: SQL dialect unification through dbt adapters, portable business logic between warehouses. Defines the architecture for supporting real-time and batch transformations on a unified platform.

Architects the AI-augmented analytics engineering workflow: auto-generating dbt models from data contracts, AI-powered data quality monitoring, natural language to SQL for self-service analytics. Defines the strategy for LLM adoption in the analytics platform.

Architects enterprise data observability: unified monitoring of all analytics pipelines, ML-driven anomaly detection, automated incident response. Defines SLO/SLI metrics for the analytics platform and post-mortem processes.

Architects review processes for enterprise analytics: cross-team review for shared models, architectural decision records for data modeling choices. Defines governance for production-ready models and data certification processes.

Architects the enterprise data documentation platform: unified portal with dbt docs, BI glossary, and data contracts. Defines the data literacy strategy: onboarding for new analysts, self-service data discovery, automated documentation via AI.

Architects the version control strategy for enterprise analytics: dbt mesh with cross-project dependencies, Git-based data contracts, automated release pipelines. Defines approaches for managing hundreds of dbt models across distributed teams.